The conjugate gradient method can handle very large sparse matrices, where direct
methods (such as LU decomposition) are way too expensive to be useful in practice.
Such large sparse matrices arise naturally in many engineering problems, such as
in ASIC placement algorithms and when solving partial differential equations.